G017444651

IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. IV (July – Aug. 2015), PP 46-51
www.iosrjournals.org
DOI: 10.9790/0661-17444651 www.iosrjournals.org 46 | Page
Data Hiding Inside the Image Using Uniform Embedding
Techniques
J. S. Harini, Student1
,
1
(Computer Science and Engineering, Sitams College / JNTUA, INDIA)
I. Introduction
The main aim of steganography is to hide data in the text media such as image, text, audio and video so
that it cannot be identified by others. One of the advantages of steganography is that it can be used to transmit
the secret messages without knowing the fact of transmission being discovered.
In existing there are three effective methods applied to image steganography are
1. Least Significant Bit (LSB) substitution
2. Blocking
3. Palette Modification.
The process of checking the bit of pixels into carrier image is known as LSB substitution.LSB is the
most powerful steganographic method which lends itself.
To maintain the confidentiality and integrity of data to prevent them from unauthorized persons.
Transmitting the military data and data related to economic, industrial organization towards the information
pertaining the institutions of finance and business must be confidential for transferring of data. As a result there
is a need of information hiding which explains the general methods and techniques used in the area of security
and confidentiality of information security for transferring them from one end to other this is one kind of
problem emerged by the various means of communication.
Encryption is the most important method used for transferring confidential data from one format to
another format using certain was by the sender and the recipient restores it to the original form. The term
steganography was derived from Greek terminology. The term stegano means hiding or restricting and the term
graphy means text, drawing or writing. This method was existing from the Greeks age which was later
developed on the advancement of communication. With the advancement of communication security has
become an important issue. Steganography is used to hide the secret data inside other objects such as text
images audio and video to protect the confidential data. After hiding the image it should be seen as singular
object and as well as materialistic. The intruder will feel difficult to discover the stego object upon its
transferring among the internet until it reaches extraction of secret data.
Problem Definition:
The art and science of hiding information by embedding messages with in other terminology harmless
messages. Steganography works by replacing bits of useless or unused data in regular computer file (such as
graphics, format, text, and html) with bits of different invisible information. There is a need for an algorithm to
embed the message. This algorithm uses simple or complex LSB embedding in the spatial domain. The hiding
process starts with embedding bits of the message in to the image. In LSB insertion technique we use the least
significant bit to embed the message. To improve the embedding capacity, two or more bits in a pixel are used
to embed messages by changing the LSB of pixel in a sequential order.
The techniques that are available today are invisible to a human senses and most of it takes a gray color
images as a special case. In order to embed a text message using a color image as a cover, two or more aspects
must be considered:
1. Choose the pixel to embed.
2. Using a randomization technique by changing the LSB pixels to embed messages.
Objectives:
The main objectives are:
1. To select the pixel for an embedding a new technique is developed and designed.
2. Reduce the size for embedding the text.
3. To resist extraction process a high degree of confidentiality is given.

Data Hiding Inside the Image Using Uniform Embedding Techniques
Significance of the problem:
A special kind of problem is categorized through the digital image steganography problem, till now,
there is no proper technique used to solve this problem, but many suggested algorithms give us good results.
The LSB of the pixels in a BMP image mainly focuses on hiding the text message.
In order to get some results about its ability to make the message store in a random way the study will
mainly focus on choosing a pixel to embed a part of the text message under a conditional value.
Since ancient times the term steganography exists and many of general assumptions apply till now. In
the area of invisible digital watermarking motivated by the desire for copyright protection of multimedia
through internet is the most of recent work undergone through steganography.
To signify origin or ownership of the digital cover signal within a signature embedded is the objective
of the digital watermarking. To embed a amount of data signified more than that of a serial number or signature
within the cover signal is the objective of steganography.
II. Literature Survey
1. Minimizing additive distortion in steganography using syndrome-trellis codes
This paper proposes a complete practical methodology for minimizing additive distortion in
steganography with general (nonbinary) embedding operation. Let every possible value of every stego element
be assigned a scalar expressing the distortion of an embedding change done by replacing the cover element by
this value. The total distortion is assumed to be a sum of per-element distortions. Both the payload-limited
sender (minimizing the total distortion while embedding a fixed payload) and the distortion-limited sender
(maximizing the payload while introducing a fixed total distortion) are considered. Without any loss of
performance, the nonbinary case is decomposed into several binary cases by replacing individual bits in cover
elements. The binary case is approached using a novel syndrome-coding scheme based on dual convolution
codes equipped with the Viterbi algorithm. This fast and very versatile solution achieves state-of-the-art results
in steganographic applications while having linear time and space complexity the number of covers elements.
We report extensive experimental results for a large set of relative payloads and for different distortion profiles,
including the wet paper channel. Practical merit of this approach is validated by constructing and testing
adaptive embedding schemes for digital images in raster and transform domains. Most current coding schemes
used in steganography (matrix embedding, wet paper codes, etc.) and many new ones can be implemented using
this framework.
2. Modified matrix encoding technique for minimal distortion steganography
It is well known that all information hiding methods that modify the least significant bits introduce
distortions into the cover objects. Those distortions have been utilized by steganalysis algorithms to detect that
the objects had been modified. It has been proposed that only coefficients whose modification does not
introduce large distortions should be used for embedding. In this paper we propose an efficient algorithm for
information hiding in the LSBs of JPEG coefficients. Our algorithm uses modified matrix encoding to choose
the coefficients whose modifications introduce minimal embedding distortion. We derive the expected value of
the embedding distortion as a function of the message length and the probability distribution of the JPEG
quantization errors of cover images. Our experiments show close agreement between the theoretical prediction
and the actual embedding distortion. Our algorithm can be used for both steganography and fragile
watermarking as well as in other applications in which it is necessary to keep the distortion as low as possible.
3. Design of adaptive steganographic schemes for digital image
Most steganographic schemes for real digital media embed messages by minimizing a suitably defined
distortion function. In practice, this is often realized by syndrome codes which offer near-optimal rate-distortion
performance. However, the distortion functions are designed heuristically and the resulting steganographic
algorithms are thus suboptimal. In this paper, we present a practical framework for optimizing the parameters of
additive distortion functions to minimize statistical detectability. We apply the framework to digital images in
both spatial and DCT domain by first defining a rich parametric model which assigns a cost of making a change
at every cover element based on its neighbourhood. Then, we present a practical method for optimizing the
parameters with respect to a chosen detection metric and feature space. We show that the size of the margin
between support vectors in soft-margin SVMs leads to a fast detection metric and that methods minimizing the
margin tend to be more secure blind steganalysis. The parameters obtained by the Nelder--Mead simplex-
reflection algorithm for spatial and DCT-domain images are presented and the new embedding methods are
tested by blind steganalyzers utilizing various feature sets. Experimental results show that as few as 80 images
are sufficient for obtaining good candidates for parameters of the cost model, which allows us to speed up the
parameter search.

4. Digital image steganography using universal distortion
Currently, the most secure practical steganographic schemes for empirical cover sources embed their
payload while minimizing a distortion function designed to capture statistical detectability. Since there exists a
general framework for this embedding paradigm with established payload-distortion bounds as well as near-
optimal practical coding schemes, building an embedding scheme has been essentially reduced to the distortion
design. This is not an easy task as relating distortion to statistical detectability is a hard and open problem. In
this article, we propose an innovative idea to measure the embedding distortion in one fixed domain
independently of the domain where the embedding changes (and coding) are carried out. The proposed universal
distortion is additive and evaluates the cost of changing an image element (e.g., pixel or DCT coefficient) from
directional residuals obtained using a Daubechies wavelet filter bank. The intuition is to limit the embedding
changes only to those parts of the cover that are difficult to model in multiple directions while avoiding smooth
regions and clean edges. The utility of the universal distortion is demonstrated by constructing steganographic
schemes in the spatial; JPEG, and side-informed JPEG domains, and comparing their security to current state-
of-the-art methods using classifiers trained with rich media models.
5. Reversible Data Embedding Using a Difference Expansion
In this paper, we present a novel reversible data embedding method for digital images. We explore the
redundancy in digital images to achieve very high embedding capacity, and keep the distortion low.
6. On Compressing Encrypted Data
In this paper, we investigate the novelty of reversing the order of these steps, i.e., first encrypting and
then compressing, without compromising either the compression efficiency or the information-theoretic
security. Although counter-intuitive, we show surprisingly that, through the use of coding with side information
principles, this reversal of order is indeed possible in some settings of interest without loss of either optimal
coding efficiency or perfect secrecy.
III. Indentations and Equations
Steganography Terminology
Steganography system is divided into two operations as detailed below:
1- Embedding process: from which it can choose the cover then put secret messages included using the stego
key that contains the secret message Stego Cover.
2- Extraction process: From which to extract the secret message using the secret key, and can be watched
through the lid are moving between the sender and the recipient by the attackers who have no right to extract the
secret message.
System Architecture

Reversible Data Hiding:
Reversible Data hiding Images is a technique, by which the original cover can be loss lessly recovered
after the embedded message is extracted. In theoretical aspect, Kalker and Willems established a rate-distortion
model for RDH, through which they proved the rate-distortion bounds of RDH for memory less covers and
proposed a recursive code construction which, however, does not approach the bound. Improved the recursive
code construction for binary covers and proved that this construction can achieve the rate-distortion bound as
long as the compression algorithm reaches entropy, which establishes the equivalence between data
compression and RDH for binary covers.
Images encryption is an effective and popular means as it converts the original and meaningful content
to incomprehensible one. Although few RDH techniques in encrypted images have been published yet, there are
some promising applications if RDH can be applied to encrypted images. In advocated a reputation-based trust
management scheme enhanced with data coloring (a way of embedding data into covers) and software
watermarking, in which data encryption and coloring offer possibilities for upholding the content owner’s
privacy and data integrity. Obviously, the cloud service provider has no right to introduce permanent distortion
during data coloring into encrypted data. Thus, a reversible data coloring technique based on encrypted data is
preferred. Suppose a medical image database is stored in a data center, and a server in the data center can embed
notations into an encrypted version of a medical image through a RDH technique. With the notations, the server
can manage the image or verify its integrity without having the knowledge of the original content, and thus the
patient’s privacy is protected. On the other hand, a doctor, having the cryptographic key, can decrypt and restore
the image in a reversible manner for the purpose of further diagnosing.
Extracting Data from Encrypted Images:
To manage and update personal information of images which are encrypted for protecting clients’
privacy, an inferior database manager may only get access to the data hiding key and have to manipulate data in
encrypted domain. The order of data extraction before image decryption guarantees the feasibility of our work in
this case. When the database manager gets the data hiding key, he can decrypt the LSB-planes of and extract the
additional data by directly reading the decrypted version. When requesting for updating information of
encrypted images, the database manager, then, updates information through LSB replacement and encrypts
updated information according to the data hiding key all over again. As the whole process is entirely operated on
encrypted domain, it avoids the leakage of original content.
Extracting Data from Decrypted Images:
In Case, both embedding and extraction of the data are manipulated in encrypted domain. On the other
hand, there is a different situation that the user wants to decrypt the image first and extracts the data from the
decrypted image when it is needed. The following example is an application for such scenario. Assume Alice
outsourced her images to a cloud server, and the images are encrypted to protect their contents. Into the
encrypted images, the cloud server marks the images by embedding some notation, including the identity of the
images’ owner, the identity of the cloud server and time stamps, to manage the encrypted images. Note that the
cloud server has no right to do any permanent damage to the images. Now an authorized user, Bob who has
been shared the encryption key and the data hiding key, downloaded and decrypted the images. Bob hoped to
get marked decrypted images, i.e., decrypted images still including the notation, which can be used to trace the
source and history of the data. The order of image decryption before/without data extraction is perfectly suitable
for this case. Next, we describe how to generate a marked decrypted image.
Image Partition:
The operator here for reserving room before encryption is a standard RDH technique, so the goal of
image partition is to construct a smoother area , on which standard RDH algorithms such as can achieve better
performance. To do that, without loss of generality, assume the original image is an 8 bits gray-scale image with
its size and pixels.
Image Encryption:
After rearranged self-embedded image, denoted by , is generated, we can encrypts to construct the
encrypted image, denoted by .With a stream cipher, the encryption version of is easily obtained. For example, a
gray value ranging from 0 to 255 can be represented by 8 bits, , such that The encrypted bits can be calculated
through exclusive- or operation where is generated via a standard stream cipher determined by the encryption
key. Finally, we embed 10 bits information into LSBs of first 10 pixels in encrypted version of to tell data hider
the number of rows and the number of bit-planes he can embed information into. Note that after image
encryption, the data hider or a third party cannot access the content of original image without the encryption
key, thus privacy of the content owner being protected.

Data Hiding in Encrypted Image:
Once the data hider acquires the encrypted image, he can embed some data into it, although he does not
get access to the original image. The embedding process starts with locating the encrypted version of , denoted
by . Since has been rearranged to the top of , it is effortless for the data hider to read 10 bits information in LSBs
of first 10 encrypted pixels. After knowing how many bit-planes and rows of pixels he can modify, the data
hider simply adopts LSB replacement to substitute the available bit-planes with additional data. Finally, the data
hider sets a label following to point out the end position of embedding process and further encrypts according to
the data hiding key to formulate marked encrypted image denoted by. Anyone who does not possess the data
hiding key could not extract the additional data.
LSB: least significant bits
The least significant bits have the useful property of changing rapidly if the number changes even
slightly. For example, if 1 (binary 00000001) is added to 3 (binary 00000011), the result will be 4 (binary
00000100) and three of the least significant bits will change (011 to 100). By contrast, the three most significant
bits stay unchanged (000 to 000).
Least significant bits are frequently employed in pseudorandom number generators, hash functions and
checksums. LSB, in all capitals, can also stand for “Least Significant Byte”. The meaning is parallel to the
above: it is the byte (or octet) in that position of a multi-byte number which has the least potential value in
Steganography a message might be hidden or encrypt with in in an image by changing least significant bit to be
the message bits then the image can be transmitted through network.lsb based Steganography is perhaps the
most simple and straight forward approach . in this will only affect each pixel by +-1, if at all ,it is generally
assumed with good reason that degradation caused by this embedding process would perceptually transparent.
hence there are number of lsb based Steganography techniques in the passive warden model as it difficult to
differentiate cover-image from stego images ,given the small changes that have been made.
There are many methods for steganography ,to hide the secret message into the image.LSB is the well
known method for data hiding. The approaches for steganography that are based on LSB can be found .The
another is PVD Method i.e. pixel-value differencing method divides the cover image into blocks and modifies
the pixel difference in each block for data embedding. Gray-level modification Steganography is a technique to
map data by modifying the gray level values of the image pixels. It uses the odd and even numbers to map data
within an image.
A Novel Steganographic Technique for Hiding Text behind Image
There are two major steps. First one is embedding the secret message in cover media using stegokey
the second one is extracting the secret message from the cover media using stegokey.For extraction the secret
message from the stego-media the recipient must have the stego-key.
Color histogram Techniques
In image processing and photography, a color histogram is a representation of the distribution of colors
in an image. For digital images, a color histogram represents the number of pixels that have colors in each of a
fixed list of color ranges, that span the image's colour, the set of all possible colors. The color histogram can be
built for any kind of color space, although the term is more often used for three-dimensional spaces
like RGB or HSV. For monochromatic images, the term intensity histogram may be used instead. For multi-
spectral images, where each pixel is represented by an arbitrary number of measurements (for example, beyond
the three measurements in RGB), the color histogram is N-dimensional, with N being the number of

measurements taken. Each measurement has its own wavelength range of the light spectrum, some of which
may be outside the visible spectrum. If the set of possible color values is sufficiently small, each of those colors
may be placed on a range by itself; then the histogram is merely the count of pixels that have each possible
color. Most often, the space is divided into an appropriate number of ranges, often arranged as a regular grid,
each containing many similar color values. The color histogram may also be represented and displayed as a
smooth function defined over the color space that approximates the pixel counts.
IV. Conclusion
The effectiveness of the proposed scheme is verified with evidence obtained from exhaustive
experiments using popular steganalyzers with various feature sets on the BOSSbase database. Compared with
prior arts, the proposed scheme gains favourable performance in terms of secure embedding capacity against
steganalysis.
Acknowledgements
The authors would like to thank the anonymous reviewers and associate editor for their comments that
greatly improved the manuscript.
I express my heartful thanks to my project guide Dr. P. NEELAKANTAN, for his guidance and
support at each and every stage of the project.
References
Theses
[1]. (Piyush Goel),Data Hiding in Digital Images: A Steganographic Paradigm
[2]. (Dr. Oleg Victorov), Enhancement of A Steganographic algorithm for Hiding Text Messages in Images
Websites
WWW.google.com
[3]. T. Filler, J. Judas, and J. Fridrich, “Minimizing additive distortion insteganography using syndrome-trellis codes,” IEEE Trans. Inf.
ForensicsSecurity, vol. 6, no. 3, pp. 920–935, Sep. 2011.(Ref no.1.)

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